LibGuides: Artificial Intelligence and Use: For Students

Use in the Classroom

It is essential for us to help students develop literacy specifically in the realm of AI. This narrower focus of modern literacy includes when and how to use AI effectively and, critically, how to use it ethically and responsibly because as technology changes, our loyalty to the Honor Code and our emphasis on critical thinking and the value of human contribution remain solid. - CNU QEP

Understanding Artificial Intelligence

Despite their broad potential, generative AI models also have several important limitations. Understanding these limitations is critical for using these technologies ethically and effectively.

Ethical Concerns

Bias and Fairness: Generative AI models can learn biases present in the training data, producing outputs that reflect, reinforce, or amplify social prejudices and stereotypes. Models can also unintentionally reinforce existing beliefs or opinions present in training data, resulting in biased outputs that align with specific ideologies.
Misinformation and Manipulation: AI-generated content can be used to create convincing fake news, deepfakes, and other forms of misinformation, leading to potential manipulation and harm.
Plagiarism and Copyright: The use of AI-generated content raises significant questions about authorship, intellectual property, and attribution, potentially leading to issues with plagiarism and copyright infringement. Plagiarism undermines the trust and credibility of individuals or organizations who inappropriately use someone else's property without permission or noted attribution.
Attribution and Accountability: Determining responsibility for AI-generated content can be challenging, raising questions about who is accountable for errors, biases, or malicious outputs.
Inequality: As AI providers move free to fee-based service models, unequal access to these tools could exacerbate existing global inequalities.

Quality and Reliability

Quality: AI outputs may contain false, misleading, or inaccurate information.
Consistency: Generative AI models can produce irrelevant or inconsistent results, even in response to the same prompt.
Superficiality: While AI can generate content, it might lack true creativity, originality, and deep understanding of complex concepts.
Degeneration: As AI-generated content fills the internet and becomes the source data on which future generations of AI are trained, the quality of AI output may degrade over time leading to "model collapse".

Data Privacy and Security

Data Exposure: The training of generative AI models requires large datasets, which could contain sensitive or private information that might be inadvertently revealed in generated outputs.
User Privacy: AI platforms may collect and retain personal data that could be used for purposes other than what was originally intended or disclosed to the user.

--adapted from https://libguides.rutgers.edu/artificial-intelligence

Possible Good Prompts!

Here are some examples of good prompts that can help you navigate AI queries and benefit your research process. Use AI as a tool for understanding what you need to be looking for. Use Primo or our databases for finding further information.

ex. I'm writing about mercury levels in fish in the oceans. What are some key points I need to address?

ex. I'm researching anxiety in college students and need to focus on important studies. What are the top 5 I need to make sure I find?

ex. I have an article I am having trouble making sense of, I will upload it. Can you summarize it for me and point out key points while keeping in mind this is a 200 level class and I am a college sophomore?

ex. Does this website contain any useful information for an academic paper I am writing on sustainable architecture. Here is the website: https://www.thespruce.com/what-is-sustainable-architecture-4846497

ex. I need to find the latest statistics on gun violence in vulnerable communities. What are some sites where I can find reliable information? Are there any studies on this topic that have been published?

ex. I am looking for legal cases regarding segregation and voting. What were the most significant cases in the U.S.?

Literature & Writing

Good prompt: "I'm analyzing the theme of isolation in The Great Gatsby. I've identified three key scenes where Gatsby appears physically separated from others. Can you help me think about what literary techniques Fitzgerald might be using in these isolation scenes and what they could symbolize?"

Why it works: Shows the student has done initial analysis and asks for help with interpretation techniques, not plot summary.

History

Good prompt: "I'm writing about the causes of WWI and have researched the assassination of Archduke Franz Ferdinand, the alliance system, and rising nationalism. I'm struggling to understand how these factors interconnected. Can you help me think through how to structure an argument about which factor was most significant?"

Why it works: Demonstrates prior research and asks for help with analysis and argumentation, not basic facts.

Science

Good prompt: "I'm studying enzyme kinetics and understand that temperature affects enzyme activity, but I'm confused about why extremely high temperatures cause activity to drop off. I know it's related to protein structure - can you help me understand the molecular mechanism behind this?"

Why it works: Shows foundational knowledge and asks for deeper conceptual understanding.

Research Methods

Good prompt: "I'm designing a survey about social media use among college students. I have my research question and basic demographic questions, but I'm unsure how to phrase questions about usage patterns without leading respondents. What are some techniques for neutral question wording?"

Why it works: Shows work in progress and asks for methodological guidance.

Philosophy/Critical Thinking

Good prompt: "I'm examining Kant's categorical imperative for an ethics paper. I understand the basic principle, but I'm having trouble applying it to modern dilemmas like AI decision-making. Can you help me think through how a Kantian might approach this issue?"

Why it works: Demonstrates understanding of core concepts and seeks help with application to new contexts.

The key pattern is that good prompts show what work the student has already done, ask for analytical help rather than basic information, and encourage deeper thinking rather than shortcuts. They treat AI as a thinking partner, not a replacement for learning.

Generating content like this can be done efficiently using a large language model, but it is important to remember to review the output carefully and acknowledge the source.

Artificial Intelligence (AI)

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.

Types of Artificial Intelligence

Chatbot - A chatbot is a software application that uses natural language processing (NLP) and machine learning to simulate conversation with humans, either via text or voice interfaces.
Generative AI - Generative artificial intelligence refers to algorithms and models that can generate new content or data, such as images, videos, music, or text, based on patterns learned from existing information.
Machine Learning (ML) - Machine learning is a subset of artificial intelligence that involves training computer systems to learn from data and improve their performance over time through experience.
Natural Language Processing (NLP) - NLP is a subfield of artificial intelligence that deals with the interaction between computers and human language, including text and speech processing, sentiment analysis, machine translation, and dialogue systems.
Large Language Model (LLM) - A large language model is a type of machine learning model that is trained on vast amounts of text data to generate language outputs that are coherent and contextually appropriate.

Large Language Models (LLMs)

Hallucination - In the context of AI, hallucination refers to the phenomenon where a model generates inaccurate or imaginary output that cannot be explained by its training data. Hallucinations can also contribute to the spread of false information, leading to negative consequences for individuals and society. These errors can lead to incorrect and potentially harmful outcomes.
Prompt - A prompt is a specific task or question that is given to an AI system to elicit a response or output.
Prompt Engineering - Prompt engineering is the process of designing and refining prompts to elicit desired responses or behaviors from AI systems, in order to improve their performance and versatility.

Understanding Large Language Models (LLMs)

Persona - A persona is a consistent character, personality, or role that a large language model adopts during interactoins, defined by specific traits, knowledge domains, communication sytles, and behavioral patterns that shape how it responds to users.
Tokens - In Natural Language Processing and machine learning, tokens refer to individual words or phrases in a text dataset, which are used as input features for models to analyze and understand the meaning of the text.
Training Data - Training data is the set of examples or inputs used to train an AI system, which helps the model learn patterns and relationships in the data and make predictions or decisions.